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import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
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from jupyterthemes import jtplot
jtplot.style(theme = 'monokai', context = 'notebook', ticks = True, grid = False)
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life_expectancy_df = pd.read_csv('Life_Expectancy_Data.csv')
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life_expectancy_df
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sns.heatmap(life_expectancy_df.isnull(), yticklabels = False, cbar = False, cmap="Blues")
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life_expectancy_df.info()
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life_expectancy_df.describe()
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life_expectancy_df.hist(bins = 30, figsize = (20, 20), color = 'y');
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plt.figure(figsize = (20,20))
sns.pairplot(life_expectancy_df)
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sns.scatterplot(data = life_expectancy_df, x = 'Schooling', y = 'Life expectancy ')
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sns.scatterplot(data = life_expectancy_df, x = 'GDP', y = 'Life expectancy ')
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sns.scatterplot(data = life_expectancy_df, x = 'Income composition of resources', y = 'Life expectancy ')
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sns.scatterplot(data = life_expectancy_df, x = ' HIV/AIDS', y = 'Life expectancy ')
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sns.scatterplot(data = life_expectancy_df, x = 'Income composition of resources', y = 'Life expectancy ')
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plt.figure(figsize = (20,20))
corr_matrix = life_expectancy_df.corr()
sns.heatmap(corr_matrix, annot = True)
plt.show()
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life_expectancy_df
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life_expectancy_df['Status'].nunique()
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life_expectancy_df = pd.get_dummies(life_expectancy_df, columns = ['Status'])
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life_expectancy_df
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life_expectancy_df.isnull().sum()[np.where(life_expectancy_df.isnull().sum() != 0)[0]]
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life_expectancy_df = life_expectancy_df.apply(lambda x: x.fillna(x.mean()),axis=0)
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life_expectancy_df.isnull().sum()[np.where(life_expectancy_df.isnull().sum() != 0)[0]]
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X = life_expectancy_df.drop(columns = ['Life expectancy '])
y = life_expectancy_df[['Life expectancy ']]
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X
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y
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X.shape
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y.shape
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X = np.array(X).astype('float32')
y = np.array(y).astype('float32')
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X
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from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
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from sklearn.preprocessing import StandardScaler
scaler_X = StandardScaler()
X_train = scaler_X.fit_transform(X_train)
X_test = scaler_X.transform(X_test)
scaler_y = StandardScaler()
y_train = scaler_y.fit_transform(y_train)
y_test = scaler_y.transform(y_test)
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from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, accuracy_score
regresssion_model_sklearn = LinearRegression(fit_intercept = True)
regresssion_model_sklearn.fit(X_train, y_train)
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regresssion_model_sklearn_accuracy = regresssion_model_sklearn.score(X_test, y_test)
regresssion_model_sklearn_accuracy
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print('Linear Model Coefficient (m): ', regresssion_model_sklearn.coef_)
print('Linear Model Coefficient (b): ', regresssion_model_sklearn.intercept_)
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y_predict = regresssion_model_sklearn.predict(X_test)
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y_predict
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plt.plot(y_test, y_predict, "^", color = 'y')
plt.xlabel('Model Predictions')
plt.ylabel('True Values')
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y_predict_orig = scaler_y.inverse_transform(y_predict)
y_test_orig = scaler_y.inverse_transform(y_test)
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plt.plot(y_test_orig, y_predict_orig, "^", color = 'y')
plt.xlabel('Model Predictions')
plt.ylabel('True Values')
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from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
from math import sqrt
k = X_test.shape[1]
n = len(X_test)
RMSE = float(format(np.sqrt(mean_squared_error(y_test_orig, y_predict_orig)),'.3f'))
MSE = mean_squared_error(y_test_orig, y_predict_orig)
MAE = mean_absolute_error(y_test_orig, y_predict_orig)
r2 = r2_score(y_test_orig, y_predict_orig)
adj_r2 = 1-(1-r2)*(n-1)/(n-k-1)
print('RMSE =',RMSE, '\nMSE =',MSE, '\nMAE =',MAE, '\nR2 =', r2, '\nAdjusted R2 =', adj_r2)